{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制准确率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f478d082610>]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "file = './log/lr0.03.log'\n",
    "res = []\n",
    "with open(file) as f:\n",
    "    line = f.readline()\n",
    "    while line:\n",
    "        splitted = line.split('accuracy : ')\n",
    "        if len(splitted) > 1:\n",
    "            num = float(splitted[1][:-2])\n",
    "            res.append(num)\n",
    "        line = f.readline()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.title(file.split('/')[-1])\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('precision')\n",
    "plt.plot(res)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制 Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f478cd31650>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = []\n",
    "with open(file) as f:\n",
    "    line = f.readline()\n",
    "    while line:\n",
    "        splitted = line.split('average loss : ')\n",
    "        if len(splitted) > 1:\n",
    "            num = float(splitted[1].split(' , ')[0])\n",
    "            res.append(num)\n",
    "        line = f.readline()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.title(file.split('/')[-1])\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('loss')\n",
    "plt.plot(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
